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» Incremental Learning of Variable Rate Concept Drift
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SAC
2005
ACM
15 years 3 months ago
Learning decision trees from dynamic data streams
: This paper presents a system for induction of forest of functional trees from data streams able to detect concept drift. The Ultra Fast Forest of Trees (UFFT) is an incremental a...
João Gama, Pedro Medas, Pedro Pereira Rodri...
KDD
1998
ACM
190views Data Mining» more  KDD 1998»
15 years 1 months ago
Time Series Forecasting from High-Dimensional Data with Multiple Adaptive Layers
This paper describes our work in learning online models that forecast real-valued variables in a high-dimensional space. A 3GB database was collected by sampling 421 real-valued s...
R. Bharat Rao, Scott Rickard, Frans Coetzee
SDM
2007
SIAM
198views Data Mining» more  SDM 2007»
14 years 11 months ago
Learning from Time-Changing Data with Adaptive Windowing
We present a new approach for dealing with distribution change and concept drift when learning from data sequences that may vary with time. We use sliding windows whose size, inst...
Albert Bifet, Ricard Gavaldà
ICMCS
2007
IEEE
155views Multimedia» more  ICMCS 2007»
15 years 3 months ago
Hidden Maximum Entropy Approach for Visual Concept Modeling
Recently, the bag-of-words approach has been successfully applied to automatic image annotation, object recognition, etc. The method needs to first quantize an image using the vis...
Sheng Gao, Joo-Hwee Lim, Qibin Sun
JNCA
2007
136views more  JNCA 2007»
14 years 9 months ago
Adaptive anomaly detection with evolving connectionist systems
Anomaly detection holds great potential for detecting previously unknown attacks. In order to be effective in a practical environment, anomaly detection systems have to be capable...
Yihua Liao, V. Rao Vemuri, Alejandro Pasos